scholarly journals International Real Estate Review

2016 ◽  
Vol 19 (2) ◽  
pp. 249-264
Author(s):  
Steve P. Fraser ◽  
◽  
Marcus T. Allen ◽  
Keyword(s):  
Real Estate ◽  
Income Tax ◽  
Residential Development ◽  
Development Project ◽  
Golf Course ◽  
Golf Club ◽  
Development Projects ◽  
The Real Estate ◽  
Real Estate Price ◽  
Price Premiums

Considerable prior research confirms the existence of real estate price premiums associated with golf course amenities in residential development projects. This study examines a unique residential development project in which membership in a golf club is appurtenant to the real estate: ownership of certain (but not all) dwellings in the project includes deeded membership in the project¡¦s golf club. In this development project, golf memberships can only be obtained or disposed of by acquiring or selling the associated dwelling, respectively. The results of this analysis indicates that price premiums associated with appurtenant golf memberships, after controlling for golf course view and other relevant property characteristics, are significantly positive. Furthermore, the results indicate that the magnitude of the price premium for appurtenant golf memberships varies across dwelling types (detached vs. attached) in this project. These findings may be important for housing developers, consumers, lenders, appraisers, and property and income tax authorities.

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

Study on the Influence Factors of Real Estate Price Based on Regression Model — The Case of Jilin City

2011 ◽  
Vol 117-119 ◽  
pp. 1547-1551
Author(s):  
Xi Li Tan ◽  
Han Zhou ◽  
Ying Song Xu
Keyword(s):  
Real Estate ◽  
Regression Model ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Factors Affecting ◽  
The Real Estate ◽  
Real Estate Price ◽  
Spss Software ◽  
Countermeasures And Suggestions ◽  
The Impact

Real estate price has been one of the hottest discussion topics, especially in recent years, it becomes the focus of attention. In this paper, we aim to study the impact of economic factors on real estate price. By multiple linear regression model and SPSS software, we analyze four economic indicators affecting the real estate price of Jilin city, and make some amendments and testings, the conclusions show the consumption level and housing construction area are important factors affecting the price trend. On this basis, we further to give the corresponding countermeasures and suggestions.

Land Value Evaluation of Real Estate Development Project Based on Grey Correlation Analysis

2013 ◽  
Vol 438-439 ◽  
pp. 1782-1785
Author(s):  
Wei Ran Wang ◽  
Yun Xiu Sai ◽  
Xing Fang
Keyword(s):  
Real Estate ◽  
Correlation Analysis ◽  
Influence Factors ◽  
Development Project ◽  
Real Estate Development ◽  
Land Value ◽  
Analysis Model ◽  
The Real Estate ◽  
Grey Correlation ◽  
Grey Correlation Analysis

The accuracy of land value directly affects the size of the project development profit in real estate development project, more will be related to the project success and failure. According to the characteristics of the real estate development, land prices in the mature real estate, on the basis of influence factors, the grey correlation analysis model is set up by using the value of developed successful plot, to estimate the land value, and combined with examples to provide a reference for scientific decision-making to real estate developers.

Monetary Policy's Influence on Real Estate Price in China: An Analysis Based on VAR Model

2013 ◽  
Vol 850-851 ◽  
pp. 1003-1007
Author(s):  
Xiong Song He ◽  
Guo Lin Deng
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Real Estate ◽  
Money Supply ◽  
Var Model ◽  
Response Analysis ◽  
Interest Rate Policy ◽  
The Real Estate ◽  
Real Estate Price ◽  
Rate Policy

Monetary policy has a significant effect on real estate price, and monetary policymakers need to have quick response. Based on the assumptions that monetary policy and real estate price influence each other and variables affect one another with a lag, A VAR model is designed and modified. Through impulse-response analysis and variance analysis, the influence of money supply and that of interest rate on real estate price are tested and compared. We found that: both money supply and interest rate could affect the real estate price; interest rate has a bigger influence that money supply does; as time goes on, the influence of money supply changes little, but that of interest rate enhances; interest rate policy is not easy to control and it will lead to a fluctuation of economy and the fluctuation may enhance, money supply is a better method to regulate real estate industry instead.

scholarly journals Cjenovni balon na tržištu nekretnina u Republici Hrvatskoj

2019 ◽  
Vol 9 (1) ◽  
pp. 78-88
Author(s):  
Josipa Mustać
Keyword(s):  
United States ◽  
Real Estate ◽  
Rental Housing ◽  
The United States ◽  
Significant Drop ◽  
The Real Estate ◽  
Real Estate Price ◽  
Real Estate Prices ◽  
Year 2000 ◽  
The City

The market balloons are fast-growing price phenomena, followed by their dramatic drop. In some parts of Croatia - the coastline and in the city of Zagreb, real estate prices have been growing drastically, considering the period from the year 2000. The global economic crisis occurred in the United States came 2008 due to the inflation of real estate prices, which also transferred to the Croatian economy due to the flooding effect from one market to another. This paper examines whether the same case is happening in Croatia today, namely whether the real estate price increase in Croatia was justified or they are balloons that will suddenly break. Real estate prices in Croatia are growing due to several factors, such as increased real estate demand for tourist rental, housing loans subsidies for young people and increased real estate demand by foreigners. If there is a significant drop in tourist activity in Croatia, real estate prices could fall dramatically.

scholarly journals Capital Gain Expectations And Efficiency In The Real Estate Markets

2011 ◽  
Vol 6 (4) ◽  
Author(s):  
Shady Kholdy ◽  
Ahmad Sohrabian
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Prices ◽  
Housing Price ◽  
Capital Gain ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Economic Fundamentals

Capital gain expectation is known to be an important determinant of housing price hikes during the real estate booms. Empirically, however, specifying the way expectations about current and future economic variables are formed is a dilemma. Although it is reasonable to assume that economic fundamentals have a significant effect on the investors’ expectation about future gains, a number of housing market analysts claim that expectations of housing prices are extrapolative. This study attempts to investigate the mechanism by which investors’ capital gain expectations and psychology are shaped. The results suggest that housing prices are predictable with respect to capital gain expectations only when these expectations are formed by extrapolation of past price appreciations. Considering the large number of empirical evidence on housing market anomaly with respect to capital gain expectations, the results suggest that the extrapolative expectations can better explain the real estate price behavior than expectations that are formed by economic fundamentals.

scholarly journals Interactions of Economic Development, Government Intervention and Real Estate Price Based on the Analysis of the Impulse-response Function

2017 ◽  
Vol 6 (2) ◽  
pp. 163
Author(s):  
Shengnan Zhao ◽  
Hong Xu
Keyword(s):  
Economic Development ◽  
Real Estate ◽  
Response Function ◽  
Government Intervention ◽  
Impulse Response Function ◽  
Housing Price ◽  
The Real Estate ◽  
Real Estate Price ◽  
Development Level ◽  
The Government

Using the PVAR model, impulse-response function and variance decomposition, this paper analyzes the interaction between Chinese economic development level, government intervention degree and real estate price, based on the inter-provincial panel data from 2000 to 2013 of China. The results show that the economic development level and the marketization degree have a positive impact on housing price, and in the long run, the self-regulation of marketization is the main factor that affecting the housing price; the government intervention can effectively curb the promotion of real estate price, but if the enthusiasm of the government to implement the intervention is not high, the excessive price of the real estate will hinder the Chinese economic development. The Chinese government should accurately grasp the relationship between real estate price and macroeconomic factors, playing a role in the market to promote the healthy and orderly development of the real estate market. 

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